Prognostic Scoring Systems in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) remains a leading cause of cancer mortality worldwide, partly owing to the heterogeneity of tumour biology and underlying liver dysfunction. Prognostic scoring systems have been developed to integrate tumour burden, vascular invasion, liver functional reserve and patient performance status into composite indices that guide treatment selection and predict outcomes. Traditional models, such as the Child-Turcotte-Pugh score, combine clinical and laboratory parameters but suffer from subjective elements. More recent approaches leverage objective metrics of hepatic function, notably the albumin–bilirubin (ALBI) grade and the platelet–albumin–bilirubin (PALBI) score, which stratify patients with minimal overlap across risk categories. Nomograms and multivariable models incorporate additional factors—such as α-fetoprotein levels and radiological features—to improve individualised risk estimates. Machine learning techniques have further refined prognostic accuracy by detecting nonlinear interactions among variables. Across treatment modalities—resection, loco-regional therapies, systemic agents and immunotherapy—these scoring systems underpin patient selection, enable risk stratification in clinical trials and support shared decision-making in multidisciplinary care.

Research from Nature Portfolio

An influential comparative analysis assessed twelve noninvasive liver reserve models in a large cohort of patients undergoing surgical resection. It demonstrated that the King’s score offered superior discrimination for tumour recurrence, while the ALBI grade outperformed other indices in predicting overall survival. Multivariate analysis confirmed that ALBI grade, alongside established factors such as tumour size, vascular invasion and performance status, independently predicts long-term outcome. This work has become foundational in validating the ALBI grade as a standard measure of hepatic functional reserve across international surgical centres, reinforcing its adoption in clinical guidelines and prospective trials.

Prognostic Scoring Systems in Hepatocellular Carcinoma publication trend

The graph below shows the total number of articles in prognostic scoring systems in hepatocellular carcinoma across all publications each year (not limited to Nature Index journals).

Technical terms

Nomogram: A graphical representation of a statistical model that yields individualized risk estimates by aligning patient variables on calibrated axes.

Concordance index (C-index): A measure of predictive accuracy for time-to-event models, indicating the probability that predicted and observed outcomes are concordant.

Area under the curve (AUC): A metric derived from receiver operating characteristic analysis that quantifies discriminative ability of a binary classifier across all thresholds.

Machine learning: A set of computational algorithms that detect complex patterns and interactions in data to build predictive models without explicit programming of relationships.

Albumin–bilirubin (ALBI) grade: An objective score of liver functional reserve calculated from serum albumin and bilirubin levels, stratified into three risk categories.

Platelet–albumin–bilirubin (PALBI) score: An extension of the ALBI grade that incorporates platelet count to account for portal hypertension and splenic function.

References

1. Machine learning-based model for predicting tumor recurrence after interventional therapy in HBV-related hepatocellular carcinoma patients with low preoperative platelet-albumin-bilirubin score. Frontiers in Immunology (2024).
2. A novel liver-function-indicators-based prognosis signature for patients with hepatocellular carcinoma treated with anti-programmed cell death-1 therapy. Cancer Immunology, Immunotherapy (2024).
3. Assessment of Albumin-Incorporating Scores at Hepatocellular Carcinoma Diagnosis Using Machine Learning Techniques: An Evaluation of Prognostic Relevance. Bioengineering (2024).
4. Comparison of twelve liver functional reserve models for outcome prediction in patients with hepatocellular carcinoma undergoing surgical resection. Scientific Reports (2018).